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<a href="_rprop_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment"> * </span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> *</span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> * \brief       implements different versions of Resilient Backpropagation of error.</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> * </span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> * \author      Oswin Krause</span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> * \date        2010</span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> *</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> *</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> * </span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * </span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published </span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * </span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * </span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> *</span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> */</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span> </div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="preprocessor">#ifndef SHARK_ML_OPTIMIZER_RPROP_H</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span><span class="preprocessor">#define SHARK_ML_OPTIMIZER_RPROP_H</span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span> </div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_single_objective_optimizer_8h.html">shark/Algorithms/AbstractSingleObjectiveOptimizer.h</a>&gt;</span></div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span> </div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a>{</div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span><span class="comment"></span> </div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="comment"> *  \brief This class offers methods for the usage of the</span></div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span><span class="comment"> *         Resilient-Backpropagation-algorithm with/out weight-backtracking.</span></div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="comment"> *</span></div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span><span class="comment"> *  The Rprop algorithm is an improvement of the algorithms with adaptive</span></div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span><span class="comment"> *  learning rates, which use increments for the update</span></div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="comment"> *  of the weights which are independent from the absolute partial</span></div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="comment"> *  derivatives. This makes sense, because large flat regions</span></div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span><span class="comment"> *  in the search space (plateaus) lead to small absolute partial</span></div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="comment"> *  derivatives and so the increments are chosen small, but the increments</span></div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="comment"> *  should be large to skip the plateau. In contrast, the absolute partial</span></div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span><span class="comment"> *  derivatives are very large at the &quot;slopes&quot; of very &quot;narrow canyons&quot;,</span></div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span><span class="comment"> *  which leads to large increments that will skip the minimum lying</span></div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span><span class="comment"> *  at the bottom of the canyon, but it would make more sense to</span></div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span><span class="comment"> *  chose small increments to hit the minimum.</span></div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span><span class="comment"> *</span></div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span><span class="comment"> *  So, the Rprop algorithm only uses the signs of the partial derivatives</span></div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span><span class="comment"> *  and not the absolute values to adapt the parameters. &lt;br&gt;</span></div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span><span class="comment"> *  Instead of individual learning rates, it uses the parameter</span></div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span><span class="comment"> *  \f$\Delta_i^{(t)}\f$ for weight \f$w_i,\ i = 1, \dots, n\f$ in</span></div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span><span class="comment"> *  iteration \f$t\f$, where the parameter will be adapted before the</span></div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span><span class="comment"> *  change of the weights: &lt;br&gt;</span></div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span><span class="comment"> *</span></div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span><span class="comment"> *  \f$</span></div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span><span class="comment"> *  \Delta_i^{(t)} = \Bigg\{</span></div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span><span class="comment"> *  \begin{array}{ll}</span></div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span><span class="comment"> *  min( \eta^+ \cdot \Delta_i^{(t-1)}, \Delta_{max} ), &amp; \mbox{if\ }</span></div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span><span class="comment"> *  \frac{\partial E^{(t-1)}}{\partial w_i} \cdot</span></div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span><span class="comment"> *  \frac{\partial E^{(t)}}{\partial w_i} &gt; 0 \\</span></div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span><span class="comment"> *  max( \eta^- \cdot \Delta_i^{(t-1)}, \Delta_{min} ), &amp; \mbox{if\ }</span></div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span><span class="comment"> *  \frac{\partial E^{(t-1)}}{\partial w_i} \cdot</span></div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span><span class="comment"> *  \frac{\partial E^{(t)}}{\partial w_i} &lt; 0 \\</span></div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span><span class="comment"> *  \Delta_i^{(t-1)}, &amp; \mbox{otherwise}</span></div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span><span class="comment"> *  \end{array}</span></div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span><span class="comment"> *  \f$</span></div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span><span class="comment"> *</span></div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span><span class="comment"> *  The parameters \f$\eta^+ &gt; 1\f$ and \f$0 &lt; \eta^- &lt; 1\f$ control</span></div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span><span class="comment"> *  the speed of the adaptation. To stabilize the increments, they are</span></div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span><span class="comment"> *  restricted to the interval \f$[\Delta_{min}, \Delta_{max}]\f$. &lt;br&gt;</span></div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span><span class="comment"> *  After the adaptation of the \f$\Delta_i\f$ the update for the</span></div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span><span class="comment"> *  weights will be calculated as</span></div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span><span class="comment"> *</span></div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span><span class="comment"> *  \f$</span></div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span><span class="comment"> *  \Delta w_i^{(t)} := - \mbox{sign}</span></div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span><span class="comment"> *  \left( \frac{\partial E^{(t)}}{\partial w_i}\right) \cdot \Delta_i^{(t)}</span></div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span><span class="comment"> *  \f$</span></div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span><span class="comment"> *</span></div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span><span class="comment"> * There are several variants of the algorithm depending on what happens</span></div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span><span class="comment"> * when the optimum is overstepped, i.e. a sign change of the gradient occurs</span></div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span><span class="comment"> * and/or the new objective value is larger than the old.</span></div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span><span class="comment"> *</span></div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span><span class="comment"> *  Weight-backtracking can be used to increase the</span></div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span><span class="comment"> *  stability of the method.</span></div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span><span class="comment"> *  if \f$\frac{\partial E^{(t-1)}}{\partial w_i} \cdot</span></div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span><span class="comment"> *  \frac{\partial E^{(t)}}{\partial w_i} &lt; 0\f$ then</span></div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span><span class="comment"> *  \f$\Delta w_i^{(t)} := - \Delta w_i^{(t-1)}\f$.</span></div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span><span class="comment"> *  This heuristic can be improved by further taking the value of the last iteration</span></div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span><span class="comment"> *  into ccount: only undo an updated if the sign changed and the new function value </span></div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span><span class="comment"> *  is worse than the last. The idea of this modification is, that a change of the sign of the</span></div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span><span class="comment"> *  partial derivation \f$\frac{\partial E}{\partial w_i}\f$</span></div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span><span class="comment"> *  only states, that a minimum was skipped and not, whether this step</span></div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span><span class="comment"> *  lead to an approach to the minimum or not.</span></div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span><span class="comment"> *</span></div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span><span class="comment"> *  Furthermore, it has been shown to be beneficial to use gradient freezing</span></div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span><span class="comment"> *  when the rgadient changes sign, i.e. ,</span></div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span><span class="comment"> *  if \f$\frac{\partial E^{(t-1)}}{\partial w_i} \cdot</span></div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span><span class="comment"> *  \frac{\partial E^{(t)}}{\partial w_i} &lt; 0\f$ then</span></div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span><span class="comment"> *  \f$\frac{\partial E^{(t)}}{\partial w_i} := 0\f$;</span></div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span><span class="comment"> * Thus, after an unsuccessful step is performed, delta is not changed</span></div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span><span class="comment"> * for one iteration.</span></div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span><span class="comment"> *</span></div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span><span class="comment"> * Based on this, 4 major algorithms can be derived:</span></div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span><span class="comment"> * Rprop-: (no backtracking, no freezing)</span></div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span><span class="comment"> * IRprop-: (no backtracking, freezing)</span></div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span><span class="comment"> * Rprop+: (gradient based backtracking, freezing)</span></div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span><span class="comment"> * IRprop+: (function value based backtracking, freezing)</span></div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span><span class="comment"> *</span></div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span><span class="comment"> * By default, IRprop+ is chosen.</span></div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span><span class="comment"> *</span></div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span><span class="comment"> *  For further information about the algorithm, please refer to: &lt;br&gt;</span></div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span><span class="comment"> *</span></div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span><span class="comment"> *  Martin Riedmiller and Heinrich Braun, &lt;br&gt;</span></div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span><span class="comment"> *  &quot;A Direct Adaptive Method for Faster Backpropagation Learning: The</span></div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span><span class="comment"> *  RPROP Algorithm&quot;. &lt;br&gt;</span></div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span><span class="comment"> *  In &quot;Proceedings of the IEEE International Conference on Neural Networks&quot;,</span></div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span><span class="comment"> *  pp. 586-591, &lt;br&gt;</span></div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span><span class="comment"> *  Published by IEEE Press in 1993</span></div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span><span class="comment"> *</span></div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span><span class="comment"> *  Martin Riedmiller, &lt;br&gt;</span></div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span><span class="comment"> *  &quot;Advanced Supervised Learning in Multi-layer Perceptrons -</span></div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span><span class="comment"> *  From Backpropagation to Adaptive Learning Algorithms&quot;. &lt;br&gt;</span></div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span><span class="comment"> *  In &quot;International Journal of Computer Standards and Interfaces&quot;, volume 16,</span></div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span><span class="comment"> *  no. 5, 1994, pp. 265-278 &lt;br&gt;</span></div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span><span class="comment"> *</span></div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span><span class="comment"> *  Christian Igel and Michael H&amp;uuml;sken, &lt;br&gt;</span></div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span><span class="comment"> *  &quot;Empirical Evaluation of the Improved Rprop Learning Algorithm&quot;. &lt;br&gt;</span></div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span><span class="comment"> *  In Neurocomputing Journal, 2002, in press &lt;br&gt;</span></div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span><span class="comment"> * \ingroup gradientopt</span></div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span><span class="comment"> */</span></div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span><span class="keyword">template</span>&lt;<span class="keyword">class</span> SearchPo<span class="keywordtype">int</span>Type = RealVector&gt;</div>
<div class="foldopen" id="foldopen00138" data-start="{" data-end="};">
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html">  138</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_rprop.html" title="This class offers methods for the usage of the Resilient-Backpropagation-algorithm with/out weight-ba...">Rprop</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_single_objective_optimizer.html" title="Base class for all single objective optimizer.">AbstractSingleObjectiveOptimizer</a>&lt;SearchPointType &gt;</div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>{</div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html#ae68ade4b0906576bad469acb71cd679e">  141</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_objective_function.html" title="Super class of all objective functions for optimization and learning.">AbstractObjectiveFunction&lt;SearchPointType,double&gt;</a> <a class="code hl_typedef" href="classshark_1_1_rprop.html#ae68ade4b0906576bad469acb71cd679e">ObjectiveFunctionType</a>;</div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html#a390d96027842e4f13ff3a1a32853951b">  142</a></span>    <a class="code hl_function" href="classshark_1_1_rprop.html#a390d96027842e4f13ff3a1a32853951b">Rprop</a>();</div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span><span class="comment"></span> </div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00145" data-start="{" data-end="}">
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html#ae04b0f8744beddb91f16038954ee34cf">  145</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_rprop.html#ae04b0f8744beddb91f16038954ee34cf" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;Rprop&quot;</span>; }</div>
</div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span> </div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html#aa5283be5eb772fcdad29af346c98b498">  148</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_rprop.html#aa5283be5eb772fcdad29af346c98b498">init</a>(<a class="code hl_class" href="classshark_1_1_abstract_objective_function.html">ObjectiveFunctionType</a> <span class="keyword">const</span>&amp; objectiveFunction, <a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#a85f0d04fdfb094dba4dc80b1fb5e3adb">SearchPointType</a> <span class="keyword">const</span>&amp; startingPoint);</div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html#a015c8b2cbc204bd39b2ef89500679625">  149</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_rprop.html#a015c8b2cbc204bd39b2ef89500679625">init</a>(<a class="code hl_class" href="classshark_1_1_abstract_objective_function.html">ObjectiveFunctionType</a> <span class="keyword">const</span>&amp; objectiveFunction, <a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#a85f0d04fdfb094dba4dc80b1fb5e3adb">SearchPointType</a> <span class="keyword">const</span>&amp; startingPoint, <span class="keywordtype">double</span> initDelta);</div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>    <span class="keyword">using </span><a class="code hl_class" href="classshark_1_1_abstract_single_objective_optimizer.html" title="Base class for all single objective optimizer.">AbstractSingleObjectiveOptimizer</a>&lt;<a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#a85f0d04fdfb094dba4dc80b1fb5e3adb">SearchPointType</a> &gt;<a class="code hl_function" href="classshark_1_1_rprop.html#aa5283be5eb772fcdad29af346c98b498">::init</a>;</div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span> </div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html#a9173edb5b7a84bcd46b62a46445754a6">  152</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_rprop.html#a9173edb5b7a84bcd46b62a46445754a6">step</a>(<a class="code hl_class" href="classshark_1_1_abstract_objective_function.html">ObjectiveFunctionType</a> <span class="keyword">const</span>&amp; objectiveFunction);</div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span> </div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html#ac754e595049b6dbbc114ac9c0f54ce0b">  154</a></span>    <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_rprop.html#ac754e595049b6dbbc114ac9c0f54ce0b" title="Read the component from the supplied archive.">read</a>( <a class="code hl_typedef" href="namespaceshark.html#ada68729491840669e47c8ad42282424f" title="Type of an archive to read from.">InArchive</a> &amp; archive );</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html#aa3eeff5854571f527c46558397f4e23b">  155</a></span>    <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_rprop.html#aa3eeff5854571f527c46558397f4e23b" title="Write the component to the supplied archive.">write</a>( <a class="code hl_typedef" href="namespaceshark.html#af4f8eb8e9618f5236b71bbcb12b8a524" title="Type of an archive to write to.">OutArchive</a> &amp; archive ) <span class="keyword">const</span>;</div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span><span class="comment"></span> </div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span><span class="comment">    //! set decrease factor</span></div>
<div class="foldopen" id="foldopen00158" data-start="{" data-end="}">
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html#a73397b4ec932c2fff8cde203f05e75df">  158</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_rprop.html#a73397b4ec932c2fff8cde203f05e75df" title="set decrease factor">setEtaMinus</a>(<span class="keywordtype">double</span> etaMinus) {</div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>        <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a>( etaMinus &lt; 1 );</div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>        <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a>( etaMinus &gt; 0 );</div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span>        <a class="code hl_variable" href="classshark_1_1_rprop.html#a8e529c7460eee4b633db970572450ac0" title="The decrease factor , set to 0.5 by default.">m_decreaseFactor</a> = etaMinus;</div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span>    }</div>
</div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span><span class="comment"></span> </div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span><span class="comment">    //! set increase factor</span></div>
<div class="foldopen" id="foldopen00165" data-start="{" data-end="}">
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html#af14caea0cc918c17a285ab4ec1ed37df">  165</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_rprop.html#af14caea0cc918c17a285ab4ec1ed37df" title="set increase factor">setEtaPlus</a>(<span class="keywordtype">double</span> etaPlus) {</div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span>        <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a>( etaPlus &gt; 1 );</div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span>        <a class="code hl_variable" href="classshark_1_1_rprop.html#acfea7fd8e09c841959fedf30c05e89e4" title="The increase factor , set to 1.2 by default.">m_increaseFactor</a> = etaPlus;</div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span>    }</div>
</div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span><span class="comment"></span> </div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span><span class="comment">    //! set upper limit on update</span></div>
<div class="foldopen" id="foldopen00171" data-start="{" data-end="}">
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html#aaa0ed6fd2ea27d56ef0cb203b1ef21d2">  171</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_rprop.html#aaa0ed6fd2ea27d56ef0cb203b1ef21d2" title="set upper limit on update">setMaxDelta</a>(<span class="keywordtype">double</span> d) {</div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span>        <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a>( d &gt; 0 );</div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span>        <a class="code hl_variable" href="classshark_1_1_rprop.html#aeb42fc2e0045b5fe4ef3583c9ab879d2" title="The upper limit of the increments , set to 1e100 by default.">m_maxDelta</a> = d;</div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span>    }</div>
</div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span><span class="comment"></span> </div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span><span class="comment">    //! set lower limit on update</span></div>
<div class="foldopen" id="foldopen00177" data-start="{" data-end="}">
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html#aa6efebd4d0dd0203239434574e6fbd70">  177</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_rprop.html#aa6efebd4d0dd0203239434574e6fbd70" title="set lower limit on update">setMinDelta</a>(<span class="keywordtype">double</span> d) {</div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span>        <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a>( d &gt;= 0 );</div>
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span>        <a class="code hl_variable" href="classshark_1_1_rprop.html#a4f24ed0314c79c6cf94d0436f3b220c4" title="The lower limit of the increments , set to 0.0 by default.">m_minDelta</a> = d;</div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span>    }</div>
</div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span>    </div>
<div class="foldopen" id="foldopen00182" data-start="{" data-end="}">
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html#af828e4f52722599bda4f244599699d68">  182</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_rprop.html#af828e4f52722599bda4f244599699d68">setUseOldValue</a>(<span class="keywordtype">bool</span> useOldValue){</div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span>        <a class="code hl_variable" href="classshark_1_1_rprop.html#a013068bdc4b644904c14f0b732173af3">m_useOldValue</a> = useOldValue;</div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span>        <span class="keywordflow">if</span>(<a class="code hl_variable" href="classshark_1_1_rprop.html#a013068bdc4b644904c14f0b732173af3">m_useOldValue</a>)</div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span>            this-&gt;<a class="code hl_variable" href="classshark_1_1_abstract_optimizer.html#a72daf583d406e144b90869f311baa594">m_features</a> |= this-&gt;<a class="code hl_enumvalue" href="classshark_1_1_abstract_optimizer.html#a77bf437afee3445601c680cc652410f0af46b9e1111a0858df3670fe12e4ffbf0">REQUIRES_VALUE</a>;</div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span>        <span class="keywordflow">else</span></div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span>            this-&gt;<a class="code hl_variable" href="classshark_1_1_abstract_optimizer.html#a72daf583d406e144b90869f311baa594">m_features</a>.<a class="code hl_function" href="classshark_1_1_typed_flags.html#a68f0c572adf112b680ef11531aa9ffb8">reset</a>(this-&gt;<a class="code hl_enumvalue" href="classshark_1_1_abstract_optimizer.html#a77bf437afee3445601c680cc652410f0af46b9e1111a0858df3670fe12e4ffbf0">REQUIRES_VALUE</a>);</div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span>    }</div>
</div>
<div class="foldopen" id="foldopen00189" data-start="{" data-end="}">
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html#adf4586f45e4dfdab3dc01ed08e96e076">  189</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_rprop.html#adf4586f45e4dfdab3dc01ed08e96e076">setUseFreezing</a>(<span class="keywordtype">bool</span> useFreezing){</div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span>        <a class="code hl_variable" href="classshark_1_1_rprop.html#af99be2b46efa0f35e4e9ce63aa046c8f">m_useFreezing</a> = useFreezing;</div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span>    }</div>
</div>
<div class="foldopen" id="foldopen00192" data-start="{" data-end="}">
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html#a560914675f5b0cfbc089f5afb2143780">  192</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_rprop.html#a560914675f5b0cfbc089f5afb2143780">setUseBacktracking</a>(<span class="keywordtype">bool</span> useBacktracking){</div>
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span>        <a class="code hl_variable" href="classshark_1_1_rprop.html#a21e63bfe6371ae23ada9529230a0b473">m_useBacktracking</a> = useBacktracking;</div>
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno">  194</span>    }</div>
</div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span><span class="comment"></span> </div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span><span class="comment">    //! return the maximal step size component</span></div>
<div class="foldopen" id="foldopen00197" data-start="{" data-end="}">
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html#a7ca10945bb7ef8a73f53512ff25a77a1">  197</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_rprop.html#a7ca10945bb7ef8a73f53512ff25a77a1" title="return the maximal step size component">maxDelta</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno">  198</span>        <span class="keywordflow">return</span> *std::max_element(<a class="code hl_variable" href="classshark_1_1_rprop.html#affadb8b3614b3b8ff1c5a94a4e3878b5" title="The absolute update values (increment) for all weights.">m_delta</a>.begin(),<a class="code hl_variable" href="classshark_1_1_rprop.html#affadb8b3614b3b8ff1c5a94a4e3878b5" title="The absolute update values (increment) for all weights.">m_delta</a>.end());</div>
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno">  199</span>    }</div>
</div>
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno">  200</span>    <span class="comment"></span></div>
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno">  201</span><span class="comment">    /// \brief Returns the derivative at the current point. Can be used for stopping criteria.</span></div>
<div class="foldopen" id="foldopen00202" data-start="{" data-end="}">
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html#a4758301af0b01ee2d2025d66f3bc0885">  202</a></span><span class="comment"></span>    <a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#a85f0d04fdfb094dba4dc80b1fb5e3adb">SearchPointType</a> <span class="keyword">const</span>&amp; <a class="code hl_function" href="classshark_1_1_rprop.html#a4758301af0b01ee2d2025d66f3bc0885" title="Returns the derivative at the current point. Can be used for stopping criteria.">derivative</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno">  203</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_rprop.html#a678dae5fcefbec650622cf4700ef8e8d">m_derivative</a>;</div>
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno">  204</span>    }</div>
</div>
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno">  205</span><span class="keyword">protected</span>:</div>
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html#a678dae5fcefbec650622cf4700ef8e8d">  206</a></span>    <a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#a85f0d04fdfb094dba4dc80b1fb5e3adb">SearchPointType</a> <a class="code hl_variable" href="classshark_1_1_rprop.html#a678dae5fcefbec650622cf4700ef8e8d">m_derivative</a>;</div>
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno">  207</span><span class="comment"></span> </div>
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno">  208</span><span class="comment">    //! The increase factor \f$ \eta^+ \f$, set to 1.2 by default.</span></div>
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html#acfea7fd8e09c841959fedf30c05e89e4">  209</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_variable" href="classshark_1_1_rprop.html#acfea7fd8e09c841959fedf30c05e89e4" title="The increase factor , set to 1.2 by default.">m_increaseFactor</a>;</div>
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno">  210</span><span class="comment"></span> </div>
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno">  211</span><span class="comment">    //! The decrease factor \f$ \eta^- \f$, set to 0.5 by default.</span></div>
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html#a8e529c7460eee4b633db970572450ac0">  212</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_variable" href="classshark_1_1_rprop.html#a8e529c7460eee4b633db970572450ac0" title="The decrease factor , set to 0.5 by default.">m_decreaseFactor</a>;</div>
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno">  213</span><span class="comment"></span> </div>
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno">  214</span><span class="comment">    //! The upper limit of the increments \f$ \Delta w_i^{(t)} \f$, set to 1e100 by default.</span></div>
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html#aeb42fc2e0045b5fe4ef3583c9ab879d2">  215</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_variable" href="classshark_1_1_rprop.html#aeb42fc2e0045b5fe4ef3583c9ab879d2" title="The upper limit of the increments , set to 1e100 by default.">m_maxDelta</a>;</div>
<div class="line"><a id="l00216" name="l00216"></a><span class="lineno">  216</span><span class="comment"></span> </div>
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno">  217</span><span class="comment">    //! The lower limit of the increments \f$ \Delta w_i^{(t)} \f$, set to 0.0 by default.</span></div>
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html#a4f24ed0314c79c6cf94d0436f3b220c4">  218</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_variable" href="classshark_1_1_rprop.html#a4f24ed0314c79c6cf94d0436f3b220c4" title="The lower limit of the increments , set to 0.0 by default.">m_minDelta</a>;<span class="comment"></span></div>
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno">  219</span><span class="comment">    //! The last function value observed</span></div>
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html#ae0aa5e8b04e94f5c47dc2f282a8cfe31">  220</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_variable" href="classshark_1_1_rprop.html#ae0aa5e8b04e94f5c47dc2f282a8cfe31" title="The last function value observed.">m_oldValue</a>;</div>
<div class="line"><a id="l00221" name="l00221"></a><span class="lineno">  221</span> </div>
<div class="line"><a id="l00222" name="l00222"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html#a00c8b33da2ef3ac08a9fa0f3c66bdeb1">  222</a></span>    <span class="keywordtype">size_t</span> <a class="code hl_variable" href="classshark_1_1_rprop.html#a00c8b33da2ef3ac08a9fa0f3c66bdeb1">m_parameterSize</a>;</div>
<div class="line"><a id="l00223" name="l00223"></a><span class="lineno">  223</span><span class="comment"></span> </div>
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno">  224</span><span class="comment">    //! The last error gradient.</span></div>
<div class="line"><a id="l00225" name="l00225"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html#a5aaadcfbb66e181642e5458031fab3d0">  225</a></span><span class="comment"></span>    <a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#a85f0d04fdfb094dba4dc80b1fb5e3adb">SearchPointType</a> <a class="code hl_variable" href="classshark_1_1_rprop.html#a5aaadcfbb66e181642e5458031fab3d0" title="The last error gradient.">m_oldDerivative</a>;<span class="comment"></span></div>
<div class="line"><a id="l00226" name="l00226"></a><span class="lineno">  226</span><span class="comment">    //! the step eprformed last. used for weight backtracking</span></div>
<div class="line"><a id="l00227" name="l00227"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html#a97c2b98a403a2dcfa4b57140e85d0b74">  227</a></span><span class="comment"></span>    <a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#a85f0d04fdfb094dba4dc80b1fb5e3adb">SearchPointType</a> <a class="code hl_variable" href="classshark_1_1_rprop.html#a97c2b98a403a2dcfa4b57140e85d0b74" title="the step eprformed last. used for weight backtracking">m_deltaw</a>;</div>
<div class="line"><a id="l00228" name="l00228"></a><span class="lineno">  228</span><span class="comment"></span> </div>
<div class="line"><a id="l00229" name="l00229"></a><span class="lineno">  229</span><span class="comment">    //! The absolute update values (increment) for all weights.</span></div>
<div class="line"><a id="l00230" name="l00230"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html#affadb8b3614b3b8ff1c5a94a4e3878b5">  230</a></span><span class="comment"></span>    <a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#a85f0d04fdfb094dba4dc80b1fb5e3adb">SearchPointType</a> <a class="code hl_variable" href="classshark_1_1_rprop.html#affadb8b3614b3b8ff1c5a94a4e3878b5" title="The absolute update values (increment) for all weights.">m_delta</a>;</div>
<div class="line"><a id="l00231" name="l00231"></a><span class="lineno">  231</span>    </div>
<div class="line"><a id="l00232" name="l00232"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html#af99be2b46efa0f35e4e9ce63aa046c8f">  232</a></span>    <span class="keywordtype">bool</span> <a class="code hl_variable" href="classshark_1_1_rprop.html#af99be2b46efa0f35e4e9ce63aa046c8f">m_useFreezing</a>;</div>
<div class="line"><a id="l00233" name="l00233"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html#a21e63bfe6371ae23ada9529230a0b473">  233</a></span>    <span class="keywordtype">bool</span> <a class="code hl_variable" href="classshark_1_1_rprop.html#a21e63bfe6371ae23ada9529230a0b473">m_useBacktracking</a>;</div>
<div class="line"><a id="l00234" name="l00234"></a><span class="lineno"><a class="line" href="classshark_1_1_rprop.html#a013068bdc4b644904c14f0b732173af3">  234</a></span>    <span class="keywordtype">bool</span> <a class="code hl_variable" href="classshark_1_1_rprop.html#a013068bdc4b644904c14f0b732173af3">m_useOldValue</a>;</div>
<div class="line"><a id="l00235" name="l00235"></a><span class="lineno">  235</span>};</div>
</div>
<div class="line"><a id="l00236" name="l00236"></a><span class="lineno">  236</span> </div>
<div class="line"><a id="l00237" name="l00237"></a><span class="lineno">  237</span><span class="keyword">extern</span> <span class="keyword">template</span> <span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_rprop.html" title="This class offers methods for the usage of the Resilient-Backpropagation-algorithm with/out weight-ba...">Rprop&lt;RealVector&gt;</a>;</div>
<div class="line"><a id="l00238" name="l00238"></a><span class="lineno">  238</span><span class="keyword">extern</span> <span class="keyword">template</span> <span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_rprop.html" title="This class offers methods for the usage of the Resilient-Backpropagation-algorithm with/out weight-ba...">Rprop&lt;FloatVector&gt;</a>;</div>
<div class="line"><a id="l00239" name="l00239"></a><span class="lineno">  239</span> </div>
<div class="line"><a id="l00240" name="l00240"></a><span class="lineno">  240</span>}</div>
<div class="line"><a id="l00241" name="l00241"></a><span class="lineno">  241</span> </div>
<div class="line"><a id="l00242" name="l00242"></a><span class="lineno">  242</span><span class="preprocessor">#endif</span></div>
<div class="line"><a id="l00243" name="l00243"></a><span class="lineno">  243</span> </div>
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